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DeepCEST 3T: Robust neural network prediction of 3T CEST MRI parameters including uncertainty quantification
{Analysis of CEST data often requires complex mathematical modeling before contrast generation, which can be error prone and time-consuming. Here, a probabilistic deep learning approach is introduced to shortcut conventional Lorentzian fitting analysis of 3T in-vivo CEST data by learning from previously evaluated data. It is demonstrated that the trained networks generalize to data of a healthy subject and a brain tumor patient, providing CEST contrasts in a fraction of the conventional evaluation time. Additionally, the probabilistic network architecture enables uncertainty quantification, indicating if predictions are trustworthy, which is assessed by perturbation analysis.}
@misc{item_3248020, title = {{DeepCEST 3T: Robust neural network prediction of 3T CEST MRI parameters including uncertainty quantification}}, booktitle = {{2020 ISMRM \& SMRT Virtual Conference \& Exhibition}}, abstract = {{Analysis of CEST data often requires complex mathematical modeling before contrast generation, which can be error prone and time-consuming. Here, a probabilistic deep learning approach is introduced to shortcut conventional Lorentzian fitting analysis of 3T in-vivo CEST data by learning from previously evaluated data. It is demonstrated that the trained networks generalize to data of a healthy subject and a brain tumor patient, providing CEST contrasts in a fraction of the conventional evaluation time. Additionally, the probabilistic network architecture enables uncertainty quantification, indicating if predictions are trustworthy, which is assessed by perturbation analysis.}}, pages = {216}, year = {2020}, slug = {item_3248020}, author = {Glang, F and Deshmane, A and Prokudin, S and Martin, F and Herz, K and Lindig, T and Bender, B and Scheffler, K and Zaiss, M} }